SOTAVerified

Image Enhancement

Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.

Source: A Comprehensive Review of Image Enhancement Techniques

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 983 papers

TitleStatusHype
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach—0
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement—0
CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment—0
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing—0
Curved Gabor Filters for Fingerprint Image Enhancement—0
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement—0
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement—0
DarkVision: A Benchmark for Low-light Image/Video Perception—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
Debiased Subjective Assessment of Real-World Image Enhancement—0
Decompose X-ray Images for Bone and Soft Tissue—0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
Deep Bilateral Retinex for Low-Light Image Enhancement—0
Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement (JUDE)—0
Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges—0
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator—0
Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application—0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions—0
Deep Photo Cropper and Enhancer—0
Deep Symmetric Network for Underexposed Image Enhancement With Recurrent Attentional Learning—0
Deformably-Scaled Transposed Convolution—0
DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision—0
Dense residual Transformer for image denoising—0
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All datasetsMIT-Adobe 5kTIP 2018Exposure-ErrorsMIT-Adobe FiveKSICE-GradSICE-Mix

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HG-MTFEPSNR on proRGB25.69—Unverified
2PQDynamicISPPSNR on proRGB25.53—Unverified
3RSFNet-mapPSNR on proRGB25.49—Unverified
4AdaIntPSNR on proRGB25.49—Unverified
5SepLUTPSNR on proRGB25.47—Unverified
6MTFEPSNR on proRGB25.46—Unverified
73D LUTPSNR on proRGB25.21—Unverified
8RetinexformerPSNR on sRGB24.94—Unverified
94D LUTPSNR on proRGB24.61—Unverified
10DIFAR (MSCA, level 1)PSNR on proRGB24.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ESDNet-LPSNR30.11—Unverified
2MBCNNPSNR30.03—Unverified
3ESDNetPSNR29.81—Unverified
4Uformer-BPSNR29.28—Unverified
5MopNetPSNR27.75—Unverified
6DMCNNPSNR26.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Exposure-slotPSNR23.18—Unverified
2CSECPSNR22.73—Unverified
3LCDPNetPSNR22.17—Unverified
4IATPSNR20.34—Unverified
5MSECPSNR20.21—Unverified
#ModelMetricClaimedVerifiedStatus
1TreEnhanceDeltaE11.25—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.45—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.43—Unverified